The water sector has collected reams of data for decades, but it's only within the last few years that utilities, agencies, consultants and vendors have begun to use that data to improve everything from managing maintenance to predicting water flow to digitally mimicking an entire watershed. The move to leverage digital information in the sector over the last two to three years is "drastic," says Luis Casado, senior vice president of water for Gannett Fleming and one of several people who spoke passionately about the possibilities of water data at Water Environment Federation's annual WEFTEC conference Oct. 1-3 in New Orleans. Firms like Gannett Fleming, Arcadis, Brown and Caldwell, and Jacobs are taking previously underutilized information from supervisory control and data acquisition, or SCADA, systems, and pairing it with historic datasets and additional sensor data to create customized digital dashboards and applications for water agencies and related entities. "It's not a single piece of software, it's an approach of how you look at data and how you merge that information and use it effectively in day-to-day operation," said Kevin Stively, smart utility leader for Brown and Caldwell, in a presentation at the event. He said historical information can be layered on real-time information to help a younger workforce make the operational decisions that older workers relied on their "gut" to make.

Industry after industry is under siege as companies embrace digital transformation (DX) to disrupt existing business models and disintermediate their competitor's customer relationships. But what do we mean by "Digital Transformation"? Digital Transformation The coupling of granular, real-time data (e.g., smartphones, connected devices, smart appliances, wearables, mobile commerce, video surveillance) with modern technologies (e.g., cloud native apps, Big Data architectures, hyper-converged technologies, artificial intelligence, blockchain) to enhance products, processes, and business-decision making with customer, product and operational insights. The digital transformation starts by understanding the organization's business initiatives, and then prioritizing which initiatives are top candidates for enhancement through digital transformation. "Begin with an end in mind" to quote Stephen Covey.

Decades of research in artificial intelligence (AI) have produced formidable technologies that are providing immense benefit to industry, government, and society. AI systems can now translate across multiple languages, identify objects in images and video, streamline manufacturing processes, and control cars. The deployment of AI systems has not only created a trillion-dollar industry that is projected to quadruple in three years, but has also exposed the need to make AI systems fair, explainable, trustworthy, and secure. Future AI systems will rightfully be expected to reason effectively about the world in which they (and people) operate, handling complex tasks and responsibilities effectively and ethically, engaging in meaningful communication, and improving their awareness through experience. Achieving the full potential of AI technologies poses research challenges that require a radical transformation of the AI research enterprise, facilitated by significant and sustained investment. These are the major recommendations of a recent community effort coordinated by the Computing Community Consortium and the Association for the Advancement of Artificial Intelligence to formulate a Roadmap for AI research and development over the next two decades.

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis in locations close to where data is captured based on artificial intelligence. The aim of edge intelligence is to enhance the quality and speed of data processing and protect the privacy and security of the data. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this paper, we present a thorough and comprehensive survey on the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, namely edge caching, edge training, edge inference, and edge offloading, based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare and analyse the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, etc. This survey article provides a comprehensive introduction to edge intelligence and its application areas. In addition, we summarise the development of the emerging research field and the current state-of-the-art and discuss the important open issues and possible theoretical and technical solutions.